class_docstr: Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
  Detector.<https://arxiv.org/abs/2008.13367>`_.
  
  The VFNet predicts IoU-aware classification scores which mix the
  object presence confidence and object localization accuracy as the
  detection score. It is built on the FCOS architecture and uses ATSS
  for defining positive/negative training examples. The VFNet is trained
  with Varifocal Loss and empolys star-shaped deformable convolution to
  extract features for a bbox.
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      regress_ranges (tuple[tuple[int, int]]): Regress range of multiple
          level points.
      center_sampling (bool): If true, use center sampling. Default: False.
      center_sample_radius (float): Radius of center sampling. Default: 1.5.
      sync_num_pos (bool): If true, synchronize the number of positive
          examples across GPUs. Default: True
      gradient_mul (float): The multiplier to gradients from bbox refinement
          and recognition. Default: 0.1.
      bbox_norm_type (str): The bbox normalization type, 'reg_denom' or
          'stride'. Default: reg_denom
      loss_cls_fl (dict): Config of focal loss.
      use_vfl (bool): If true, use varifocal loss for training.
          Default: True.
      loss_cls (dict): Config of varifocal loss.
      loss_bbox (dict): Config of localization loss, GIoU Loss.
      loss_bbox (dict): Config of localization refinement loss, GIoU Loss.
      norm_cfg (dict): dictionary to construct and config norm layer.
          Default: norm_cfg=dict(type='GN', num_groups=32,
          requires_grad=True).
      use_atss (bool): If true, use ATSS to define positive/negative
          examples. Default: True.
      anchor_generator (dict): Config of anchor generator for ATSS.
  
  Example:
      >>> self = VFNetHead(11, 7)
      >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
      >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats)
      >>> assert len(cls_score) == len(self.scales)
docstr: Compute FCOS regression and classification targets for points in
  multiple images.
  
  Args:
      points (list[Tensor]): Points of each fpn level, each has shape
          (num_points, 2).
      gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
          each has shape (num_gt, 4).
      gt_labels_list (list[Tensor]): Ground truth labels of each box,
          each has shape (num_gt,).
  
  Returns:
      tuple:
          labels (list[Tensor]): Labels of each level.
          label_weights: None, to be compatible with ATSS targets.
          bbox_targets (list[Tensor]): BBox targets of each level.
          bbox_weights: None, to be compatible with ATSS targets.
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code: def get_fcos_targets(self, points, gt_bboxes_list, gt_labels_list):
      """Compute FCOS regression and classification targets for points in
      multiple images.
  
      Args:
          points (list[Tensor]): Points of each fpn level, each has shape
              (num_points, 2).
          gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
              each has shape (num_gt, 4).
          gt_labels_list (list[Tensor]): Ground truth labels of each box,
              each has shape (num_gt,).
  
      Returns:
          tuple:
              labels (list[Tensor]): Labels of each level.
              label_weights: None, to be compatible with ATSS targets.
              bbox_targets (list[Tensor]): BBox targets of each level.
              bbox_weights: None, to be compatible with ATSS targets.
      """
      labels, bbox_targets = FCOSHead.get_targets(
          self, points, gt_bboxes_list, gt_labels_list
      )
      label_weights = None
      bbox_weights = None
      return labels, label_weights, bbox_targets, bbox_weights
  
imports: import numpy
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
  from mmcv.ops import DeformConv2d
  from mmcv.runner import force_fp32
  from mmdet.core import bbox2distance, bbox_overlaps, build_anchor_generator, build_assigner, build_sampler, distance2bbox, multi_apply, multiclass_nms, reduce_mean
  from ..builder import HEADS, build_loss
  from .atss_head import ATSSHead
  from .fcos_head import FCOSHead
label_desc: Use image node_id 13 for calling the VFNetHead obj's underlying member method code with example usage: obj.get_fcos_targets(points, gt_bboxes_list, gt_labels_list) and returns: labels,  label_weights,  bbox_targets,  bbox_weights